This repo contains code for the different spatiotemporal methods used to detect deepfake videos on the Celeb-DF dataset. All convolutional network methods were implemented in PyTorch and were trained on the Celeb-DF v2 dataset. The networks implemented are:
- RCN
- R3D
- MC3
- R2Plus1D
- I3D
We also investigate one non-temporal classification method that is DFT based.
The ROC_AUC curve and Test accuracies for different methods are shown below. Our methods outperformed state-of-the-art frame based methods for Deepfake classification.
| ROC Curves | Test Accuracies |
|---|---|
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Best test ROC-AUC Scores and Accuracies for the spatio-temporal convolutional methods trained on Celeb-DF:
The power spectra informations for real and fake images in the Celeb-DF dataset (as obtained from the DFT method) is shown below.
Celeb-DF V2 Dataset: http://www.cs.albany.edu/~lsw/celeb-deepfakeforensics.html
RetinaFace: https://github.com/biubug6/Pytorch_Retinaface
I3D Implementation: https://github.com/piergiaj/pytorch-i3d
RCN Implementation: https://github.com/chinmay5/FakeDetection